Blind feedforward cyclostationarity-based timing estimation for linear modulations

By exploiting a general cyclostationary (CS) statistics-based framework, this letter develops a rigorous and unified asymptotic (large sample) performance analysis setup for a class of blind feedforward timing epoch estimators for linear modulations transmitted through time nonselective flat-fading channels. Within the proposed CS framework, it is shown that several estimators proposed in the literature can be asymptotically interpreted as maximum likelihood (ML) estimators applied on a (sub)set of the second- (and/or higher) order statistics of the received signal. The asymptotic variance of these ML estimators is established in closed-form expression and compared with the modified Crame/spl acute/r-Rao bound. It is shown that the timing estimator proposed by Oerder and Meyr achieves asymptotically the best performance in the class of estimators which exploit all the second-order statistics of the received signal, and its performance is insensitive to oversampling rates P as long as P/spl ges/3. Further, an asymptotically best consistent estimator, which achieves the lowest asymptotic variance among all the possible estimators that can be derived by exploiting jointly the second- and fourth-order statistics of the received signal, is also proposed.     

Reliability studies of MOCVD TiSiN and EnCoRe Ta(N)/Ta

Passivated single damascene copper SiO₂ damascene lines were evaluated in combination with TiSiN and Ta(N)/Ta diffusion barriers. Leakage current, breakdown and time-dependent dielectric breakdown properties were investigated on a wafer level basis for temperatures ranging between room temperature and 150 °C. It is found that the leakage performance of the wafers with a TiSiN barrier is better at room temperature, but at 150 °C the performance levels out with Ta(N)/Ta. Time-dependent dielectric breakdown measurements at 150 °C show that the lifetime of the interconnect is higher with the selected Ta(N)/Ta barrier than for TiSiN.     

An introduction of the condition class space with continuous value discretization and rough set theory

The granularity of an information system has an incumbent effect on the efficacy of the analysis from many machine learning algorithms. An information system contains a universe of objects characterized and categorized by condition and decision attributes. To manage the concomitant granularity, a level of continuous value discretization (CVD) is often undertaken. In the case of the rough set theory (RST) methodology for object classification, the granularity contributes to the grouping of objects into condition classes with the same condition attribute values. This article exposits the effect of a level of CVD on the subsequent condition classes constructed, with the introduction of the condition class space—the domain within which the condition classes exist. This domain elucidates the association of the condition classes to the related decision outcomes—reflecting the inexactness incumbent when a level of CVD is undertaken. A series of measures is defined that quantify this association. Throughout this study and without loss of generality, the findings are made through the RST methodology. This further offers a novel exposition of the relationship between all the condition attributes and the RST‐related reducts (subsets of condition attributes). © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 173–191, 2006.     

Platinum Binuclear Complexes as Phosphorescent Dopants for Monochromatic and White Organic Light‐Emitting Diodes

Efficient blue‐, green‐, and red‐light‐emitting organic diodes are fabricated using binuclear platinum complexes as phosphorescent dopants. The series of complexes used here have pyrazolate bridging ligands and the general formula C^∧NPt(μ‐pz)₂PtC^∧N (where C^∧N = 2‐(4′,6′‐difluorophenyl)pyridinato‐N,C^2′, pz = pyrazole ( 1 ), 3‐methyl‐5‐tert‐butylpyrazole ( 2 ), and 3,5‐bis(tert‐butyl)pyrazole ( 3 )). The Pt–Pt distance in the complexes, which decreases in the order 1 > 2 > 3 , solely determines the electroluminescence color of the organic light‐emitting diodes (OLEDs). Blue OLEDs fabricated using 8 % 1 doped into a 3,5‐bis(N‐carbazolyl)benzene (mCP) host have a quantum efficiency of 4.3 % at 120 Cd m^–2, a brightness of 3900 Cd m^–2 at 12 V, and Commission Internationale de L'Eclairage (CIE) coordinates of (0.11, 0.24). Green and red OLEDs fabricated with 2 and 3 , respectively, also give high quantum efficiencies (～ 6.7 %), with CIE coordinates of (0.31, 0.63) and (0.59, 0.46), respectively. The current‐density–voltage characteristics of devices made using dopants 2 and 3 indicate that hole trapping is enhanced by short Pt–Pt distances (< 3.1 Å). Blue electrophosphorescence is achieved by taking advantage of the binuclear molecular geometry in order to suppress dopant intermolecular interactions. No evidence of low‐energy emission from aggregate states is observed in OLEDs made with 50 % 1 doped into mCP. OLEDs made using 100 % 1 as an emissive layer display red luminescence, which is believed to originate from distorted complexes with compressed Pt–Pt separations located in defect sites within the neat film. White OLEDs are fabricated using 1 and 3 in three different device architectures, either with one or two dopants in dual emissive layers or both dopants in a single emissive layer. All the white OLEDs have high quantum efficiency (～ 5 %) and brightness (～ 600 Cd m^–2 at 10 V).     

Quantitative pharmacophore models with inductive logic programming

Three-dimensional models, or pharmacophores, describing Euclidean constraints on the location on small molecules of functional groups (like hydrophobic groups, hydrogen acceptors and donors, etc.), are often used in drug design to describe the medicinal activity of potential drugs (or ‘ligands’). This medicinal activity is produced by interaction of the functional groups on the ligand with a binding site on a target protein. In identifying structure-activity relations of this kind there are three principal issues: (1) It is often difficult to “align” the ligands in order to identify common structural properties that may be responsible for activity; (2) Ligands in solution can adopt different shapes (or `conformations’) arising from torsional rotations about bonds. The 3-D molecular substructure is typically sought on one or more low-energy conformers; and (3) Pharmacophore models must, ideally, predict medicinal activity on some quantitative scale. It has been shown that the logical representation adopted by Inductive Logic Programming (ILP) naturally resolves many of the difficulties associated with the alignment and multi-conformation issues. However, the predictions of models constructed by ILP have hitherto only been nominal, predicting medicinal activity to be present or absent. In this paper, we investigate the construction of two kinds of quantitative pharmacophoric models with ILP: (a) Models that predict the probability that a ligand is “active”; and (b) Models that predict the actual medicinal activity of a ligand. Quantitative predictions are obtained by the utilising the following statistical procedures as background knowledge: logistic regression and naive Bayes, for probability prediction; linear and kernel regression, for activity prediction. The multi-conformation issue and, more generally, the relational representation used by ILP results in some special difficulties in the use of any statistical procedure. We present the principal issues and some solutions. Specifically, using data on the inhibition of the protease Thermolysin, we demonstrate that it is possible for an ILP program to construct good quantitative structure-activity models. We also comment on the relationship of this work to other recent developments in statistical relational learning. Editors: Tamás Horváth and Akihiro Yamamoto     

Intelligent process analytics for CRM

Many organisations use decision models in their processes such as tables or trees to provide decision support to their operational divisions. For example, in fault management, customer contact centre operators usually use a decision model in the form of prescribed interviews. Based on the answers given by customers, the operator navigates through the decision model to reach an assessment of the problem. In order to achieve customer satisfaction and operational excellence, it is very important to constantly monitor the performance of a decision model not only on an overall level, but also on the level of individual decisions. In this paper we present a configurable business process analytics tool, known as the intelligent Universal Service Management System, that constantly monitors decision data and is capable of optimising the decisions based on high-level business objectives. We explain the various features of the software and show how it can be used to optimise decision processes. We also show how we can easily provide a customised version to monitor the performance of provision processes.     

Learning in multiple model adaptive control switch

Including a learning mechanism in SMMAC (switched, multiple-model adaptive control) avoids the need for a priori knowledge of the model set of the plant to control and leads to a significant performance improvement with respect to the sole inclusion of an adaptive control channel in combination with switched fixed local controllers.     

Parameter Uncertainty and Policy Intensity: Some Extensions and Suggestions for Further Work

The effects of parameter uncertainty on optimal policy have been a matter of interest for academics, and even for some policymakers, for a long time. Two lines of literature have developed analytical results on this matter. The first line uses static models and the second dynamic models. In this dynamic line most of the results are confined to models with a single state and a single control variable. In this paper we want to encourage the analysis of more general dynamic cases. To do so, the results in the dynamic line are extended from one-state and one-control finite horizon models to models with a pair of control variables. We then discuss some of the hurdles which must be surmounted for the results to be made more general and suggests some lines for further research. JEL classification: C61; E61     

Improving the Calibration of Vacuum Gas Flow – Control Leak Gauges

A new and original method of calibrating helium leaks that makes it possible to take into account the existence of a number of auxiliary phenomena is described. For this purpose, a hollow leak the purpose of which is to reproduce only desorbed gas flows, is introduced into the vacuum system of the calibration bench. Analysis of the results of corresponding cumulation measurements makes it possible to introduce corrections for these phenomena.     

Profile-Driven Component Placement for Cluster-Based Online Services

The growth of the Internet and of various intranets has spawned a wealth of online services, most of which are implemented on local-area clusters using remote invocation (for example, remote procedure call/remote method invocation) among manually placed application components. Component placement can be a significant challenge for large-scale services, particularly when application resource needs are workload dependent. Automatic component placement has the potential to maximize overall system throughput. The key idea is to construct (offline) a mapping between input workload and individual-component resource consumption. Such mappings, called component profiles, then support high-performance placement. Preliminary results on an online auction benchmark based on J2EE (Java 2 Platform, Enterprise Edition) suggest that profile-driven tools can identify placements that achieve near-optimal overall throughput.